The Secret to Data Analytics and Audit Quality

Data analytics has already made significant inroads into various aspects of finance and accounting. Business Intelligence platforms are utilising data analytics tools for planning, budgeting and reporting and audit functions are not far behind.

A survey of 150 senior audit practitioners by the ICAEW revealed that around 70% believed data analytics can improve audit quality. This is in addition to other benefits such as additional insights, cost saving, fraud detection etc. How exactly however does data analytics improve audit quality?

Exploratory and confirmatory data analytics

Exploratory data analytics is employed to find interesting patterns in existing data. This is useful because it may reveal some obscure relationship between variables not easily visible to the naked eye.

Confirmatory data analytics is deployed to look for the existence of certain predefined patterns and confirm a hypothesis. This could be useful in situations where combing through reams of data using traditional technologies would be prohibitively time consuming. Thus, rather than relying on workarounds like data sampling, confirmatory data analytics can provide a more comprehensive and decisive outcome – therefore reducing the potential risk.

Another key benefit of data analytics is its significantly increased ability to detect fraud and other operational breaches. Companies and even regulators are already using such techniques to look for suspicious patterns in high risk activity.

Making it work

Despite the benefits that data analytics clearly provides, there are certain challenges which restrict its usefulness in a general sense.

The most obvious challenge is the cost in deploying such a system. Although, with time and broader adoption, the costs are coming down. Software vendors are now developing tools and making them available to corporate clients at more affordable price points. This brings us to the second, and more long term, challenge – lack of a skilled workforce with the appropriate skillset to operate such systems. Even large, professional auditing firms are struggling with skill shortages in this field as it requires a high-level cross-disciplinary expertise.

Other challenges remain around data integrity and access. The results from data analytics depends on the quality of the data that is fed into it in the first place. Tech companies relying heavily on data analytics have their entire systems designed around the collection of meaningful data, while for most other companies it is usually a patchwork of disparate legacy systems. This means that data quality can vary wildly, and this in turn can impact audit quality.

Conclusion

The benefits of data analytics in compliance, risk management and audit are apparent. It increases audit quality and, in most cases, provides additional insight which can prove to be invaluable. However, the dearth of skilled manpower remains a key challenge This will require significant company investments in the development of technical skillsets as well as analytical mindsets on the part of practitioners.